import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
    import transformers






def 					snake_case    (			snake_case__     :str)		->  List[str]:
       random.seed(snake_case__)
       np.random.seed(snake_case__)
       torch.manual_seed(snake_case__)
       torch.cuda.manual_seed_all(snake_case__)
       # ^^ safe to call this function even if cuda is not available







class 							a				:







 """simple docstring"""






 def __init__(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = 0.9999						,							lowerCAmelCase_ = 0.0						,							lowerCAmelCase_ = 0						,							lowerCAmelCase_ = False						,							lowerCAmelCase_ = 1.0						,							lowerCAmelCase_ = 2 / 3						,							lowerCAmelCase_ = None						,							lowerCAmelCase_ = None						,							**lowerCAmelCase_						,							)    ->   List[str]:

        if isinstance(_a						,							torch.nn.Module				):
               _A	  =    (
                   """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
                   """Please pass the parameters of the module instead."""
               )
               deprecate(
                   """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""						,							"""1.0.0"""						,							_a						,							standard_warn=_a						,							)
               _A	  =    parameters.parameters()

               # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
               _A	  =    True

        if kwargs.get("""max_value"""						,							_a				) is not None:
               _A	  =    """The `max_value` argument is deprecated. Please use `decay` instead."""
               deprecate("""max_value"""						,							"""1.0.0"""						,							_a						,							standard_warn=_a				)
               _A	  =    kwargs["""max_value"""]

        if kwargs.get("""min_value"""						,							_a				) is not None:
               _A	  =    """The `min_value` argument is deprecated. Please use `min_decay` instead."""
               deprecate("""min_value"""						,							"""1.0.0"""						,							_a						,							standard_warn=_a				)
               _A	  =    kwargs["""min_value"""]

        _A	  =    list(_a				)
        _A	  =    [p.clone().detach() for p in parameters]

        if kwargs.get("""device"""						,							_a				) is not None:
               _A	  =    """The `device` argument is deprecated. Please use `to` instead."""
               deprecate("""device"""						,							"""1.0.0"""						,							_a						,							standard_warn=_a				)
               self.to(device=kwargs["""device"""]				)

        _A	  =    None

        _A	  =    decay
        _A	  =    min_decay
        _A	  =    update_after_step
        _A	  =    use_ema_warmup
        _A	  =    inv_gamma
        _A	  =    power
        _A	  =    0
        _A	  =    None  # set in `step()`

        _A	  =    model_cls
        _A	  =    model_config



 @classmethod
 def 	UpperCAmelCase				(   cls						,							lowerCAmelCase_						,							lowerCAmelCase_				)    ->   int:
        _A  ,    _A	  =    model_cls.load_config(_a						,							return_unused_kwargs=_a				)
        _A	  =    model_cls.from_pretrained(_a				)

        _A	  =    cls(model.parameters()						,							model_cls=_a						,							model_config=model.config				)

        ema_model.load_state_dict(_a				)
        return ema_model



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   Tuple:
        if self.model_cls is None:
               raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""				)

        if self.model_config is None:
               raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""				)

        _A	  =    self.model_cls.from_config(self.model_config				)
        _A	  =    self.state_dict()
        state_dict.pop("""shadow_params"""						,							_a				)

        model.register_to_config(**_a				)
        self.copy_to(model.parameters()				)
        model.save_pretrained(_a				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   str:
        _A	  =    max(0						,							optimization_step - self.update_after_step - 1				)

        if step <= 0:
               return 0.0

        if self.use_ema_warmup:
               _A	  =    1 - (1 + step / self.inv_gamma) ** -self.power
        else:
               _A	  =    (1 + step) / (10 + step)

        _A	  =    min(_a						,							self.decay				)
        # make sure decay is not smaller than min_decay
        _A	  =    max(_a						,							self.min_decay				)
        return cur_decay_value



 @torch.no_grad()
 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   List[str]:
        if isinstance(_a						,							torch.nn.Module				):
               _A	  =    (
                   """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
                   """Please pass the parameters of the module instead."""
               )
               deprecate(
                   """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""						,							"""1.0.0"""						,							_a						,							standard_warn=_a						,							)
               _A	  =    parameters.parameters()

        _A	  =    list(_a				)

        self.optimization_step += 1

        # Compute the decay factor for the exponential moving average.
        _A	  =    self.get_decay(self.optimization_step				)
        _A	  =    decay
        _A	  =    1 - decay

        _A	  =    contextlib.nullcontext
        if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
               import deepspeed

        for s_param, param in zip(self.shadow_params						,							_a				):
               if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                      _A	  =    deepspeed.zero.GatheredParameters(_a						,							modifier_rank=_a				)

               with context_manager():
                      if param.requires_grad:
                             s_param.sub_(one_minus_decay * (s_param - param)				)
                      else:
                             s_param.copy_(_a				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   Dict:
        _A	  =    list(_a				)
        for s_param, param in zip(self.shadow_params						,							_a				):
               param.data.copy_(s_param.to(param.device				).data				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_=None						,							lowerCAmelCase_=None				)    ->   List[str]:
        _A	  =    [
            p.to(device=_a						,							dtype=_a				) if p.is_floating_point() else p.to(device=_a				)
            for p in self.shadow_params
        ]



 def 	UpperCAmelCase				(   self				)    ->   Any:
        return {
            "decay": self.decay,
            "min_decay": self.min_decay,
            "optimization_step": self.optimization_step,
            "update_after_step": self.update_after_step,
            "use_ema_warmup": self.use_ema_warmup,
            "inv_gamma": self.inv_gamma,
            "power": self.power,
            "shadow_params": self.shadow_params,
        }



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   Dict:
        _A	  =    [param.detach().cpu().clone() for param in parameters]



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   List[Any]:
        if self.temp_stored_params is None:
               raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""				)
        for c_param, param in zip(self.temp_stored_params						,							_a				):
               param.data.copy_(c_param.data				)

        # Better memory-wise.
        _A	  =    None



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   Any:
        _A	  =    copy.deepcopy(_a				)

        _A	  =    state_dict.get("""decay"""						,							self.decay				)
        if self.decay < 0.0 or self.decay > 1.0:
               raise ValueError("""Decay must be between 0 and 1"""				)

        _A	  =    state_dict.get("""min_decay"""						,							self.min_decay				)
        if not isinstance(self.min_decay						,							_a				):
               raise ValueError("""Invalid min_decay"""				)

        _A	  =    state_dict.get("""optimization_step"""						,							self.optimization_step				)
        if not isinstance(self.optimization_step						,							_a				):
               raise ValueError("""Invalid optimization_step"""				)

        _A	  =    state_dict.get("""update_after_step"""						,							self.update_after_step				)
        if not isinstance(self.update_after_step						,							_a				):
               raise ValueError("""Invalid update_after_step"""				)

        _A	  =    state_dict.get("""use_ema_warmup"""						,							self.use_ema_warmup				)
        if not isinstance(self.use_ema_warmup						,							_a				):
               raise ValueError("""Invalid use_ema_warmup"""				)

        _A	  =    state_dict.get("""inv_gamma"""						,							self.inv_gamma				)
        if not isinstance(self.inv_gamma						,							(float, int)				):
               raise ValueError("""Invalid inv_gamma"""				)

        _A	  =    state_dict.get("""power"""						,							self.power				)
        if not isinstance(self.power						,							(float, int)				):
               raise ValueError("""Invalid power"""				)

        _A	  =    state_dict.get("""shadow_params"""						,							_a				)
        if shadow_params is not None:
               _A	  =    shadow_params
               if not isinstance(self.shadow_params						,							_a				):
                      raise ValueError("""shadow_params must be a list"""				)
               if not all(isinstance(_a						,							torch.Tensor				) for p in self.shadow_params				):
                      raise ValueError("""shadow_params must all be Tensors"""				)










"""simple docstring"""




import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
lowerCAmelCase				:     List[str]					=				object()

# For specifying empty leaf dict `{}`
lowerCAmelCase				:     Any					=				object()



def        a__					(							snake_case__			,					snake_case__							)		->   List[Any]:
			lowerCamelCase      =   tuple((re.compile(x + """$"""							) for x in qs)							)
			for i in range(len(snake_case__							) - len(snake_case__							) + 1							):
						lowerCamelCase      =   [x.match(snake_case__							) for x, y in zip(snake_case__			,					ks[i:]							)]
						if matches and all(snake_case__							):
									return True
			return False



def        a__					(							snake_case__							)		->   str:
			def replace(snake_case__			,					snake_case__							):
						for rule, replacement in rules:
									if _match(snake_case__			,					snake_case__							):
												return replacement
						return val

			return replace



def        a__					(							)		->   Union[str, Any]:
			return [
			    # embeddings
			    (("transformer", "wpe", "embedding"), P("""mp"""			,					snake_case__							)),
			    (("transformer", "wte", "embedding"), P("""mp"""			,					snake_case__							)),
			    # atention
			    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(snake_case__			,					"""mp"""							)),
			    (("attention", "out_proj", "kernel"), P("""mp"""			,					snake_case__							)),
			    (("attention", "out_proj", "bias"), None),
			    # mlp
			    (("mlp", "c_fc", "kernel"), P(snake_case__			,					"""mp"""							)),
			    (("mlp", "c_fc", "bias"), P("""mp"""							)),
			    (("mlp", "c_proj", "kernel"), P("""mp"""			,					snake_case__							)),
			    (("mlp", "c_proj", "bias"), None),
			    # layer norms
			    ((r"ln_\d+", "bias"), None),
			    ((r"\d+", r"ln_\d+", "scale"), None),
			    (("ln_f", "bias"), None),
			    (("ln_f", "scale"), None),
			]



def        a__					(							snake_case__							)		->   Optional[Any]:
			lowerCamelCase      =   _get_partition_rules()
			lowerCamelCase      =   _replacement_rules(snake_case__							)
			lowerCamelCase      =   {k: _unmatched for k in flatten_dict(snake_case__							)}
			lowerCamelCase      =   {k: replace(snake_case__			,					snake_case__							) for k, v in initd.items()}
			assert _unmatched not in result.values(), "Incomplete partition spec."
			return freeze(unflatten_dict(snake_case__							)							)



